Gas Exchange of Apple and Blackberry Leaves Treated with a Kaolin Particle Film on Adaxial, Abaxial, or Both Leaf Surfaces
Bibliographic record
Abstract
Kaolin particle films are used as a means of pest control in some commercial apple orchards in the Maritime provinces; however, no studies to date have evaluated the impact of these particle films on leaf gas exchange under the region's growing conditions. Also previously unexplored is the gas exchange response of blackberry leaves to kaolin particle films and the question of whether leaf gas exchange response varies according to the leaf surface of particle film application. A study consisting of an apple field trial and a blackberry greenhouse trial was conducted during the 2005 growing season in Bouctouche, New Brunswick, Canada, with the aims of 1) characterizing the leaf temperature and gas exchange responses [net photosynthesis, stomatal conductance ( g s ), intercellular CO 2 , and transpiration] of ‘Ginger Gold’ apple [ Malus × sylvestris (L.) Mill var. domestica (Borkh.) Mansf.] leaves to a kaolin particle film (95% kaolin clay) applied at various leaf residue densities under the province's growing conditions, 2) characterizing the leaf temperature and gas exchange responses of ‘Triple Crown’ blackberry ( Rubus L. subgenus Rubus Watson) leaves to treatment of adaxial or abaxial surfaces with the kaolin particle film at various leaf residue densities, and 3) determining whether the gas exchange response of apple and blackberry leaves to the kaolin particle film varies according to leaf temperature. Leaf gas exchange measurements were taken under conditions of ambient CO 2 , saturated light, moderate (apple) or high (blackberry) relative humidity levels and leaf temperatures ranging from 26 to 39 °C (apple) and 15 to 41 °C (blackberry). When the particle film was applied to both the adaxial and abaxial surfaces of apple leaves at kaolin residue densities of 0.5 to 3.7 g·m −2 , leaf temperature was reduced by up to 1.1 °C ( P = 0.005) and g s was increased ( P = 0.029) relative to leaves with trace (<0.5 g·m −2 ) levels of kaolin deposits. No other effects of kaolin leaf residue density on apple leaf gas exchange were found, nor were any interactions of leaf temperature × residue level ( P > 0.05). When applied to a fixed area on the adaxial or abaxial surfaces of blackberry leaves at kaolin residue densities of 0.5 to 10.8 g·m −2 , the particle film did not alter leaf temperature or gas exchange ( P > 0.05). No interactions of leaf temperature × residue level or leaf temperature × leaf surface × residue level were found to affect blackberry leaf gas exchange ( P > 0.05).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".